Uncertainty Gating for Cost-Aware Explainable Artificial Intelligence
Post-hoc explanation methods are widely used to interpret black-box predictions, but their generation is often computationally expensive and their reliability is not guaranteed. We propose epistemic uncertainty as a low-cost proxy for explanation reliability: high epistemic uncertainty identifies regions where the decision boundary is poorly defined and where explanations become unstable and unfaithful. This insight enables two complementary use cases: `improving worst-case explanations' (routing samples to cheap or expensive XAI methods based on expected explanation reliability), and `recalling high-quality explanations' (deferring explanation generation for uncertain samples under const...
| Authors | Georgii Mikriukov, Grégoire Montavon, Marina M. -C. Höhne |
|---|---|
| Primary category | cs.AI |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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